New research explores how Large Language Models (LLMs) acquire and retain factual knowledge during pretraining. LLMs learn facts through multiple encounters, with deduplicated data aiding knowledge retention. Studies show smaller batch sizes improve fact acquisition.
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries Finds that language models exhibit a strong bias toward taking a "shortcut" by prioritizing the external context. 📝https://t.co/Ba2TxqGugp https://t.co/I7bBKdYlyR
From RAG to Rich Parameters Investigates more closely how LLMs utilize external knowledge over parametric information for factual queries. Finds that in a RAG pipeline, LLMs take a “shortcut” and display a strong bias towards utilizing only the context information to answer the… https://t.co/Nyh4K5XyIk
How Do Large Language Models Acquire Factual Knowledge During Pretraining? ◼ 🔍 New study reveals how large language models (LLMs) learn facts during pretraining. Surprisingly, more data doesn't boost knowledge retention! Key findings: Smaller batch sizes increase fact… https://t.co/Tv34uXjmQb
How Do Large Language Models Acquire Factual Knowledge During Pretraining? - LLMs learn facts by encountering them multiple times during training (different sources). - LLMs forget faster with exact data repetitions, using deduplicated data helps retain knowledge. - Adding more… https://t.co/Rg4S8Pdf4f
🚨 New paper 🚨 How Large Language Models Acquire Factual Knowledge During Pretraining? I’m thrilled to announce the release of my new paper! 🎉 This research explores how LLMs acquire and retain factual knowledge during pretraining. Here are some key insights: https://t.co/iK1MkwVhF2